Search without warrant based on ‘reasonable grounds’ - data overview

The dataset in scope was four years of data - 2018, 2019, 2022 and 2023 - summarising 52654 instances in which Victoria Police stopped and searched individuals without a warrant. The legal basis of the stop and search was a ‘reasonable grounds’ for the officer to suspect that the person was in possession of one of the following items:

The dataset includes individual searches and includes a range of information such as racial appearance of the person being searched, item suspected to be present, whether a prohibited item was found, rank of officer, reporting station and whether the search related to a person ‘P’ or a vehicle ‘V’.

Note that 2020 and 2021 data is not included in this analysis due to the extraordinary circumstances of the COVID 19 pandemic. Stop and search data from this period is not a sound basis for which to make inferences about stop and search behaviour, including profiling, in non-pandemic periods.

Hit Rate Analysis of Victoria Police ‘reasonable grounds’ searches

One way that racial profiling by police can be identified is through examining the reasonableness of police searches. For the searches examined by this analysis, police are not authorised to search a person unless they hold a suspicion, based on reasonable grounds, that a person is in possession of a prohibited item. When police perform a ‘reasonable grounds’ search we can infer (it is only an inference) that it is more likely the police had reasonable grounds for suspicion if they ‘find’ a contraband item following the search. This inference is not reasonable in individual cases and there are many examples of police finding a contraband item following an unreasonable search. However, over a large number of searches, the find rate provides a widely accepted mechanism to assess the overall reasonableness of police searches.

Hit rate of searches over time

In the years in scope, 17.1 percent of ‘reasonable grounds’ searches resulted in a ‘find’. The number of searches varied minimally over the period.

Year Total searches Finds Hit rate
2018 12841 2275 17.7
2019 13281 2366 17.8
2022 14987 2453 16.4
2023 11545 1958 17.0

Racial appearance data oveview

Victoria Police records the ethnic appearance of the people that they search without warrant. The racial appearance of persons who were subject to a search used in this analysis appears in Figure 1 below. The groupings were consistent with the current racial appearance categories used by Victoria Police.

A total of 10,657 search records, or twenty percent of all records, did not have any racial appearance data entered, despite this field being mandatory. Records where the officer was unable to determine the racial appearance were marked as ‘Other’.

Note that the aggregation of persons of Mediterranean and Middle Eastern appearance into a single category presents a challenge, as we would ordinarily expect persons of Italian, Greek, Croatian ancestry etc to be considered ‘non-racialised’, whereas persons in the Middle Eastern appearance would be more likely to be considered racialised, and potentially subject to islamophobia, depending on faith. The Police Accountability Project urges Victoria Police to disaggregate these categories.

Note also that police perception of racial appearance will not identify police-targeting of white appearing Aboriginal people. The results for Aboriginal people therefore must be read with caution.

Hit rate by racial appearance

Analysing police search decisions in response to police perception of a person’s racial background is a reasonable method for detecting bias in people whose ethnicity corresponds to appearance.

The hit rate by racial appearance is summarised in Table X below

Racial appearance Hit rate Finds Total searches
White 18.0 3474 19325
Middle Eastern/Med 13.8 551 3999
Asian 13.7 301 2190
African 12.9 217 1677
Aboriginal 15.6 151 966
Pacific Islander 18.8 146 776
South Asian 13.9 98 704
South American 15.7 18 115
Other 15.8 124 785
Missing 18.0 3972 22117 
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.

Data Frame Summary

VicPol station data

Dimensions: 52654 x 8
Duplicates: 49899
Variable Stats / Values Freqs (% of Valid) Graph Missing
Year [factor]
1. 2022
2. 2019
3. 2018
4. 2023
14987(28.5%)
13281(25.2%)
12841(24.4%)
11545(21.9%)
 0 (0.0%)
Area.type [factor]
1. Metro
2. Regional
27163(51.6%)
25491(48.4%)
 0 (0.0%)
Unit.type [factor]
1. Uniform
2. Public Order Response
3. Transit
4. Other
5. CIU
6. DRU
7. PSO
8. Highway Patrol
36893(70.1%)
4673(8.9%)
3755(7.1%)
2093(4.0%)
1979(3.8%)
1737(3.3%)
958(1.8%)
566(1.1%)
 0 (0.0%)
Contact.Type [factor]
1. P
2. V
31870(65.7%)
16622(34.3%)
 4162 (7.9%)
Racial.appearance [factor]
1. Missing
2. White
3. Middle Eastern/Med
4. Asian
5. African
6. Aboriginal
7. Other
8. Pacific Islander
9. South Asian
10. South American
22117(42.0%)
19325(36.7%)
3999(7.6%)
2190(4.2%)
1677(3.2%)
966(1.8%)
785(1.5%)
776(1.5%)
704(1.3%)
115(0.2%)
 0 (0.0%)
Gender [factor]
1. M
2. F
3. U
30106(81.2%)
6965(18.8%)
17(0.0%)
 15566 (29.6%)
Rank.of.Member [factor]
1. CONST
2. SCONST
3. PSO
4. SGT
5. SSGT
6. RECRUT
7. SUPT
29114(55.3%)
17968(34.1%)
3041(5.8%)
2431(4.6%)
93(0.2%)
6(0.0%)
1(0.0%)
 0 (0.0%)
Any.items.found [factor]
1. 0
2. 1
43602(82.8%)
9052(17.2%)
 0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.4.1)
2024-09-24

Finds by characteristics

An analysis of the hit-rate for features of interest appears below.

label

variable

Any.items.found

Total

test

0

1

Racial.appearance

Aboriginal

815 (84.4%)

151 (15.6%)

966 (1.8%)

p value: <0.0001
(Pearson's Chi-squared test)

African

1460 (87.1%)

217 (12.9%)

1677 (3.2%)

Asian

1889 (86.3%)

301 (13.7%)

2190 (4.2%)

Middle Eastern/Med

3448 (86.2%)

551 (13.8%)

3999 (7.6%)

Missing

18145 (82.0%)

3972 (18.0%)

22117 (42.0%)

Other

661 (84.2%)

124 (15.8%)

785 (1.5%)

Pacific Islander

630 (81.2%)

146 (18.8%)

776 (1.5%)

South American

97 (84.3%)

18 (15.7%)

115 (0.2%)

South Asian

606 (86.1%)

98 (13.9%)

704 (1.3%)

White

15851 (82.0%)

3474 (18.0%)

19325 (36.7%)

Total

43602 (82.8%)

9052 (17.2%)

52654 (100.0%)

Unit.type

CIU

1695 (85.6%)

284 (14.4%)

1979 (3.8%)

p value: <0.0001
(Pearson's Chi-squared test)

DRU

1536 (88.4%)

201 (11.6%)

1737 (3.3%)

Highway Patrol

374 (66.1%)

192 (33.9%)

566 (1.1%)

Other

1742 (83.2%)

351 (16.8%)

2093 (4.0%)

PSO

639 (66.7%)

319 (33.3%)

958 (1.8%)

Public Order Response

4018 (86.0%)

655 (14.0%)

4673 (8.9%)

Transit

2821 (75.1%)

934 (24.9%)

3755 (7.1%)

Uniform

30777 (83.4%)

6116 (16.6%)

36893 (70.1%)

Total

43602 (82.8%)

9052 (17.2%)

52654 (100.0%)

Gender

F

5999 (86.1%)

966 (13.9%)

6965 (18.8%)

p value: <0.0001
(Fisher's Exact Test for Count Data)

M

24873 (82.6%)

5233 (17.4%)

30106 (81.2%)

U

17 (100.0%)

0 (0%)

17 (0.05%)

NA

12713

2853

15566

Total

43602 (82.8%)

9052 (17.2%)

52654 (100.0%)

Year

2018

10566 (82.3%)

2275 (17.7%)

12841 (24.4%)

p value: 0.0033
(Pearson's Chi-squared test)

2019

10915 (82.2%)

2366 (17.8%)

13281 (25.2%)

2022

12534 (83.6%)

2453 (16.4%)

14987 (28.5%)

2023

9587 (83.0%)

1958 (17.0%)

11545 (21.9%)

Total

43602 (82.8%)

9052 (17.2%)

52654 (100.0%)

Area.type

Metro

22792 (83.9%)

4371 (16.1%)

27163 (51.6%)

p value: <0.0001
(Pearson's Chi-squared test)

Regional

20810 (81.6%)

4681 (18.4%)

25491 (48.4%)

Total

43602 (82.8%)

9052 (17.2%)

52654 (100.0%)

Missing race by characteristics

An analysis of the percentage with racial appearance data missing by chara

label

variable

Racial.appearance.missing

Total

test

Missing

Not missing

NA

Search.type

Drugs

9564 (26.5%)

26523 (73.5%)

10753

46840 (89.0%)

p value: <0.0001
(Pearson's Chi-squared test)

Firearms

211 (34.2%)

406 (65.8%)

97

714 (1.4%)

Graffiti

90 (11.2%)

710 (88.8%)

119

919 (1.7%)

Volatile inhalation substance

3 (14.3%)

18 (85.7%)

5

26 (0.05%)

Weapons

789 (21.5%)

2880 (78.5%)

486

4155 (7.9%)

Total

10657 (25.9%)

30537 (74.1%)

11460

52654 (100.0%)

Year

2018

0 (0%)

7011 (100.0%)

5830

12841 (24.4%)

p value: <0.0001
(Pearson's Chi-squared test)

2019

0 (0%)

7651 (100.0%)

5630

13281 (25.2%)

2022

6037 (40.3%)

8950 (59.7%)

0

14987 (28.5%)

2023

4620 (40.0%)

6925 (60.0%)

0

11545 (21.9%)

Total

10657 (25.9%)

30537 (74.1%)

11460

52654 (100.0%)

Area.type

Metro

5454 (26.4%)

15230 (73.6%)

6479

27163 (51.6%)

p value: 0.0205
(Pearson's Chi-squared test)

Regional

5203 (25.4%)

15307 (74.6%)

4981

25491 (48.4%)

Total

10657 (25.9%)

30537 (74.1%)

11460

52654 (100.0%)

Region

Eastern Region

2284 (29.9%)

5350 (70.1%)

2133

9767 (26.5%)

p value: <0.0001
(Pearson's Chi-squared test)

North West Metro Region

3165 (25.3%)

9368 (74.7%)

4026

16559 (44.9%)

Southern Metro Region

1324 (28.0%)

3406 (72.0%)

1337

6067 (16.4%)

Western Region

1067 (29.6%)

2540 (70.4%)

893

4500 (12.2%)

NA

2817

9873

3071

15761

Total

10657 (25.9%)

30537 (74.1%)

11460

52654 (100.0%)

Gender

F

710 (11.1%)

5671 (88.9%)

584

6965 (18.8%)

p value: 0.0006
(Fisher's Exact Test for Count Data)

M

2666 (9.7%)

24856 (90.3%)

2584

30106 (81.2%)

U

3 (23.1%)

10 (76.9%)

4

17 (0.05%)

NA

7278

0

8288

15566

Total

10657 (25.9%)

30537 (74.1%)

11460

52654 (100.0%)

Any.items.found

0

8820 (25.7%)

25457 (74.3%)

9325

43602 (82.8%)

p value: 0.1523
(Pearson's Chi-squared test)

1

1837 (26.6%)

5080 (73.4%)

2135

9052 (17.2%)

Total

10657 (25.9%)

30537 (74.1%)

11460

52654 (100.0%)

Hit rates by LGA

## `summarise()` has grouped output by 'Area.type', 'Local.Government.Area',
## 'Total.searches', 'LGA.Hit.rate'. You can override using the `.groups`
## argument.